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首页> 外文会议>Asian conference on remote sensing;ACRS >THE ROLE OF SEASONS IN INTERFEROMETRIC DECORRELATION OVER TROPICAL WOODY VEGETATION
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THE ROLE OF SEASONS IN INTERFEROMETRIC DECORRELATION OVER TROPICAL WOODY VEGETATION

机译：季节在热带木本植物干涉干涉解相关中的作用

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Applications of Interferometric Synthetic Aperture Radar (InSAR) are often plagued by decorrelation effects, especially over forested areas. While progress has been made in this research domain, boreal forests have often been used as the reference. There has been a paucity of similar research conducted in tropical regions, which host various types of woody vegetation. In this article, the level of decorrelation is assessed for several types of plantations, including rubber, oil palm, and tea plantations, in comparison to intact tropical forest. Our primary goal is to investigate decorrelation due to seasonal dataset configuration, which may lead to a better understanding of suitable InSAR pair selection. To demonstrate the impact of seasonality, two pairs of Phased Array L-band SAR 2 (PALSAR 2) data were acquired. July-August 2015 data were used to represent dry conditions, while wet season analysis employed February-March 2016 datasets. In general, intact tropical forest and almost all plantation types displayed low InSAR correlation, which conforms with our previous understanding. The wet season was found to be an important factor in this study, which significantly reduced correlation in all types of land cover. Tea plantations, however, maintained strong correlation; hence, it is concluded that decorrelation due to seasonality was fairly low in this case. The high correlation of tea plantations was likely due to stable wetness of leaves and minimal impact of wind on the canopy. Although mature rubber trees exhibited high decorrelation effects in the dry season, the class of young rubber plantation revealed a different outcome with its fairly high correlation. The research demonstrates that seasonality plays a role in the selection of suitable InSAR datasets in tropical regions. Hence, users should focus not only on satellite parameters such as baseline and temporal lag, but also consider the impact of seasonality.
机译：干涉合成孔径雷达（InSAR）的应用通常受到去相关效应的困扰，尤其是在森林地区。尽管在该研究领域已取得进展，但北方森林常被用作参考。在热带地区开展的类似研究很少，这些地区拥有各种类型的木本植被。在本文中，与完整的热带森林相比，评估了几种人工林的去相关水平，包括橡胶，油棕和茶园。我们的主要目标是研究由于季节性数据集配置而引起的去相关，这可能会导致对合适的InSAR对选择有更好的了解。为了证明季节性的影响，获取了两对相控阵L波段SAR 2（PALSAR 2）数据。 2015年7月至8月的数据用于表示干旱状况，而雨季分析则采用2016年2月至3月的数据集。通常，完整的热带森林和几乎所有人工林类型均显示出较低的InSAR相关性，这符合我们之前的理解。在这项研究中，发现雨季是一个重要因素，这大大降低了所有类型土地覆盖的相关性。然而，茶园保持着很强的相关性。因此，可以得出结论，在这种情况下，由季节性引起的去相关性相当低。茶园的高度相关性可能是由于叶片的湿润程度稳定以及风对树冠的影响极小。尽管成熟的橡胶树在干旱季节表现出较高的去相关效应，但幼龄橡胶树的类别却显示出不同的结果，且具有较高的相关性。研究表明，季节性在热带地区选择合适的InSAR数据集方面发挥着作用。因此，用户不仅应关注诸如基线和时间滞后之类的卫星参数，还应考虑季节性的影响。

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